Frame Erasure Concealment Technique for a Bitstream-Based Feature Extractor

ABSTRACT

A frame erasure concealment technique for a bitstream-based feature extractor in a speech recognition system particularly suited for use in a wireless communication system operates to “delete” each frame in which an erasure is declared. The deletions thus reduce the length of the observation sequence, but have been found to provide for sufficient speech recognition based on both single word and “string” tests of the deletion technique.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims the priority of Provisional Application No.60/170,170, filed Dec. 10, 1999.

TECHNICAL FIELD

The present invention relates to automatic speech recognition and, moreparticularly, to a frame erasure concealment technique for use with abitstream-based feature extraction process in wireless communicationapplications.

BACKGROUND OF THE INVENTION

In the provisioning of many new and existing communication services,voice prompts are used to aid the speaker in navigating through theservice. In particular, a speech recognizing element is used to guidethe dialogue with the user through voice prompts, usually questionsaimed at defining which information the user requires. An automaticspeech recognizer is used to recognize what is being said and theinformation is used to control the behavior of the service rendered tothe user.

Modern speech recognizers make use of phoneme-based recognition, whichrelies on phone-based sub-word models to perform speaker-independentrecognition over the telephone. In the recognition process, speech“features” are computed for each incoming frame. Modern speechrecognizers also have a feature called “rejection”. When rejectionexists, the recognizer has the ability to indicate that what was uttereddoes not correspond to any of the words in the lexicon.

The users of wireless communication services expect to have access toall of the services available to the users of land-based wirelinesystems, and to receive a similar quality of service. Thevoice-activated services are particularly important to the wirelesssubscribers since the dial pad is generally away from sight when thesubscriber listens to a vocal prompt, or is out of sight when driving acar. With speech recognition, there are virtually no restrictions onmobility, because callers do not have to take their eyes off the road topunch in the keys on the terminal.

Currently, one area of research is focusing on the front-end design fora wireless speech recognition system. In general, many prior artfront-end designs fall into one of two categories, as illustrated inFIG. 1. FIG. 1( a) illustrates an arrangement 10 including a speechencoder 12 at the transmitting end, a communication channel 14 (such asa wireless channel) and a speech decoder 16 at the receiving end. Thedecoded speech is thereafter sent to EAR and also applied as an input toa speech recognition feature extractor 18, where the output fromextractor 18 is thereafter applied as an input to an automatic speechrecognizer (not shown). In a second arrangement 20 illustrated in FIG.1( b), a speech recognition feature encoder 22 is used at thetransmitting end to allow for the features themselves to be encoded andtransmitted over the (wireless) channel 24. The encoded features arethen applied as parallel inputs to both a speech decoder 26 and a speechrecognition feature extractor 28 at the receiving end, the output fromfeature extractor 28 thereafter applied as an input to an automaticspeech recognizer (not shown). This scheme is particularly useful inInternet access applications. For example, when the mel-frequencycepstral coefficients are compressed at a rate of approximately 4kbit/s, the automatic speech recognizer (ASR) at the decoder side of thecoder exhibits a performance comparable to a conventional wireline ASRsystem. However, this scheme is not able to generate synthesized speechof the quality produced by the system as shown in FIG. 1( a).

In speech coding, channel impairments are modeled by bit error insertionand frame erasure insertion devices, where the number of bit errors andframe erasures depends primarily on the noise, co-channel and adjacentchannel interference, as well as frequency-selective fading.Fortunately, most speech coders are combined with a channel coder, wherea “frame erasure” is declared if any of the most sensitive bits withrespect to the channel is in error. The speech coding parameters of anerased frame must then be extrapolated in order to generate the speechsignal for the erased frame. A family of error concealment techniquesare known in the prior art and can generally be defined as either“substitution” or “extrapolation” techniques. In general, the parametersof the erased frames are reconstructed by repeating the parameters ofthe previous frame with scaled-down gain values. In conventional speechrecognition systems, a decoded speech-based front-end uses thesynthesized speech for extracting a feature. However, in abitstream-based front-end, the parameters themselves are present.

The need remaining in the prior art, therefore, is to provide atechnique for handling frame erasures in a bitstream-based front endspeech recognition systems.

SUMMARY OF THE INVENTION

The need remaining in the prior art is addressed by the presentinvention, which relates to automatic speech recognition and, moreparticularly, to a frame erasure concealment technique for use with abitstream-based feature extraction process in wireless communicationapplications.

In accordance with the present invention, an error in a frame isdeclared if the Euclidean distance between the line spectrum pair (LSP)coefficients in adjacent frames is less than or equal to a predefinedthreshold T. In such a case, one of the frames in then simply deletedfrom the bitstream. In particular, and based on the missing featuretheory, a decoding algorithm is reformulated for the hidden Markov model(HMM) when a frame erasure is detected.

Other and further features and advantages of the present invention willbecome apparent during the course of the following discussion and byreference to the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

Referring now to the drawings,

FIGS. 1( a) and (b) illustrate, in simplified block diagram form, twoprior arrangements for exemplary wireless automatic speech recognitionsystems;

FIG. 2 illustrates, in block diagram form, the components utilized in aspeech recognition system of the present invention;

FIG. 3 is a simplified flow chart illustrating the feature extractionprocess associated with the IS-641 speech coder;

FIG. 4 contains a diagram of the procedure for extracting featureparameters directly from the bitstream in accordance with the presentinvention;

FIG. 5 illustrates an exemplary arrangement for modeling the efficacy ofthe proposed feature extractor of the present invention when comparedwith prior art arrangements;

FIG. 6 illustrates a process of the present invention used to obtainadditional “voiced” and “unvoiced” information from the bitstream;

FIG. 7 contains exemplary trajectories of adaptive codebook gain(ACG)-voiced, and fixed codebook gain (FCG)-unvoiced-parameters forspeech after processing by an IS-641 speech coder;

FIG. 8 illustrates various speech waveforms associated with theimplementation of an exemplary speech enhancement algorithm inassociation with the feature extraction process of the presentinvention;

FIG. 9 contains graphs illustrating the word error rate (WER) associatedwith various frame erasure techniques; and

FIG. 10 illustrates the ratios of processing time between a conventionalextrapolation frame erasure technique and the frame deletion method ofthe present invention.

DETAILED DESCRIPTION

A bitstream-based approach for providing speech recognition in awireless communication system in accordance with the present inventionis illustrated in FIG. 2. As shown, a system 30 utilizes a conventionalspeech encoder 32 at the transmission end, where for explanatorypurposes it will be presumed that an IS-641 speech coder is used,however, various other coders also function reliably in the arrangementof the present invention (in particular, code-excited linearprediction—CELP encoders). The encoded speech thereafter propagatesalong a (wireless) communication channel 34 and is applied assimultaneous inputs to both a speech decoder 36 and a speech recognitionfeature extractor 38, where the interaction of these various componentswill be discussed in detail below.

FIG. 3 includes a simplified block diagram of the linear predictivecoding (LPC) analysis associated with speech coding performed using anIS-641 speech coder. As shown, the speech coder first removes undesiredlow frequency components from the speech signal by application of ahighpass filter 40 with a cutoff frequency of, for example, 80 Hz. Thefiltered speech is then applied as an input to an autocorrelationfunction using an asymmetric window 42, where one side of the window ishalf of a Hamming window and the other half is a quarter period of thecosine function. The particular shape realized by this asymmetric windowis due to the limited lookahead of the speech coder for minimizing thedelay for real applications. Subsequent to the windowing, two additionalprocesses 44 are applied to the autocorrelated signal. One is defined aslag-windowing and the other is white noise correction. The former helpsto smooth the LPC spectrum so as to exhibit no sharp peaks. The whitenoise correction provides the effect of adding noise to the speechsignal and thus avoids modeling the anti-aliasing filter response athigh frequencies with the LPC coefficients. Finally, a conventional LPCrecursion is performed (block 46) with the modified autocorrelationsequence output from block 44 to form the line spectrum pair (LSP)coefficient output. A speech encoder 48 then quantizes the LSPcoefficients and transmits them as the “bit stream” output to a decoder(not shown). When the LSP coefficients are recovered at the decoderside, the decoded LSP's will be somewhat different from the unquantizedLSP's, depending on the performance of the spectral quantizer withinspeech encoder 48.

With this understanding of the encoding process within an IS-641 speechencoder, it is possible to study in detail the bitstream recognitionprocess of the present invention. Referring to FIG. 4, a procedure isillustrated for extracting cepstral coefficients from the bitstream ofan IS-641 speech coder (the bitstream being, for example, the output ofthe arrangement illustrated in FIG. 3). A single frame is illustrated inFIG. 4 and contains two major divisions. As shown, bits 1-26 are usedfor the LSP quantization while the remaining bits 27-148 are used forall residual information. In the process, the decoded LSP's are decodedfrom the first 26 bits using a inverse quantizer 50 where, for example,these LSP's represent the spectral envelope of a 30 ms speech segmentwith a frame rate of 50 Hz. In order to match to the frame rate withthat of a conventional speech recognition front-end, the output frominverse quantizer 50 is interpolated with the LSP's of the previousframe (block 52) to convert the frame rate to 100 Hz. Next, cepstralcoefficients of order 12 are obtained by performing an LSP to LPCconversion, followed by an LPC to CEP conversion (block 54). By applyinga bandpass filter 56 to the cepstral coefficients, a set of twelveweighted coefficients is obtained. The residual signal from bits 27-148,identified as “pitch information” (bits 27-52), “algebraic codebookinformation” (bits 53-120) and “codebook gains” (bits 121-148), are alsodecoded. An energy parameters is then computed by taking the logarithmto the square-sum of the residual (20 ms).

Although this description is particular to the IS-641 speech coder, itis to be understood that the feature extraction process of the presentinvention is suitable for use with any code-excited linear prediction(CELP) speech coder.

The model illustrated in FIG. 5 can be used to measure the efficacy ofthe bitstream-based system of the present invention with various otherASR techniques. Illustrated in general is an IS-641 speech encoder 60,including an analysis module 62 and a quantizer 64. An IS-641 speechdecoder 66 is also shown, separated from IS-641 speech encoder by anideal channel 68. Included within speech decoder 66 is an inversequantizer 68 and a synthesis module 70. A conventional speech signal isapplied as an input to analysis module 62 and the decoded speech willultimately exit from synthesis module 70. The location of referencepoint C0 corresponds to the placement of a conventional wireline speechrecognition system. At reference point C1, ASR is performed on a speechsignal coded using IS-641 (corresponding to the arrangement shown inFIG. 1( a)). In order to evaluate the ASR performance of the LPCanalysis method (associated with FIG. 1( b)), an ASR at location C2 canbe used with the unquantized LSP's as generated by LPC recursion process(block 46 of FIG. 3). Lastly, an ASR positioned at location C3 (directlyconverting the bitstream output of the IS-641 coder into the speechrecognition feature set) can then be used to analyze the bitstream-basedfront end arrangement of the present invention.

Tables I and II below include the speech recognition accuracies for eachASR pair, where “Cx/Cy” is defined as an ASR that is trained in Cx andthen tested in Cy:

TABLE I Word Word Error (%) String Feature Accuracy (%) Sub. Del. Ins.Accuracy (%) C0/C0 (10 ms) 96.17 1.46 0.78 1.59 68.48 C0/C0 (20 ms)95.81 1.60 0.76 1.83 66.06 C0/C1 95.16 2.09 0.95 1.79 62.31 C1/C1 94.752.38 1.01 1.86 60.20

TABLE II Word Word Error (%) String Feature Accuracy (%) Sub. Del. Ins.Accuracy (%) C2/C2 96.23 1.43 0.71 1.63 68.91 C3/C3 95.81 1.68 0.82 1.6966.48

Table I includes a comparison of the recognition accuracy for each ofthe conventional front-ends, using the ASR location identifiersdescribed above in association with FIG. 5. Alternatively, Table IIprovides a listing of the recognition accuracy of bitstream-basedfront-end speech recognition performed in accordance with the presentinvention as located in either the encoder side or decoder side of thespeech coder arrangement. Referring to Table II, comparing the C2/C2results with the C3/C3 results, it is shown that the word and stringaccuracies of C3/C3 are decreased by 12% and 8%, respectively (resultscomparable to C0/C0 with linear interpolation). It has been determinedthat this degradation is caused mainly by the LSP quantization in theIS-641 speech coder. Therefore, the arrangement of the present inventionfurther requires a method of compensating for the LSP quantizationeffects. In accordance with the present invention, unvoiced/voicedinformation is incorporated in the feature set so that the feature setas a whole can compensate for the quantization effect.

As mentioned above, in addition to the spectral envelope, a speech codermodels the excitation signal as the indices and gains of the adaptiveand fixed codebooks, where these two gains represent the “voiced”(adaptive codebook gain—ACG) and “unvoiced” (fixed codebook gain—FCG)information. These parameters are quantized and then transmitted to thedecoder. Therefore, in accordance with the present invention, it ispossible to obtain the voiced/unvoiced information directly from thebitstream. FIG. 6 illustrates an exemplary process of extracting theseadditional “voiced” and “unvoiced” parameters in the bitstream-basedfront-end of the invention. As shown, bits 121-148 in an exemplary frame(the “gain” information as shown in FIG. 4) are further divided intofour subframes, denoted SF0, SF1, SF2, and SF3, where the ACG (voiced)and FCG (unvoiced) values are computed for each subframe. Therefore,four ACG values and four FCG values are determined for each frame(blocks 70, 72). In order to generate speech recognition featureparameters from these gains, the following equations are used:

$\begin{matrix}{{{{ACG}(i)} = {\sum\limits_{k = 0}^{1}{g_{p}^{2}\left( {{2i} + k} \right)}}},\mspace{14mu} {i = 0},1} & (1) \\{{{{FCG}(i)} = {\gamma \; 10\log_{10}\left\{ {\sum\limits_{k = 0}^{1}{g_{c}^{2}\left( {{2i} + k} \right)}} \right\}}},\mspace{14mu} {i = 0},1} & (2)\end{matrix}$

where g_(p)(i) and g_(c)(i) are defined as the ACG and FCG values of thei-th subframe. In order to add the ACG and FCG values into the featurevector and maintain the same vector dimension as before, two of thetwelve LPC cepstra values in the baseline are eliminated.

FIG. 7 illustrates an example of the trajectories of the adaptivecodebook gain and fixed codebook gain for a speech waveform after it hasbeen processed by an IS-641 speech coder. FIG. 7( a) is an illustrationof an exemplary digit string, and FIG. 7( b) is the normalized energyparameter associated with this digit string. FIGS. 7( c) and 7(d)illustrated the ACG and FCG parameters, respectively, for this string.As can be seen, both the ACG and FCG exhibit temporal fluctuations.These fluctuations can be reduced by applying a smoothing technique(such as median filtering, illustrating as blocks 74 and 76 in FIG. 6).As with the typical energy parameters in speech coding, a weightingfunction (denoted as block 78 in FIG. 6 and defined as γ in Eq. (2)) canbe added to the filtered FCG parameters, where the weighting function ischosen to control the effect of the FCG parameters relative to thevarious other parameters. In one exemplary arrangement, γ may be equalto 0.1.

Table III, included below, illustrates the improved results fromincorporating the ACG and FCG parameters into the feature set. Comparedwith the baseline, the new feature set reduces the word and string errorrates by 10% for each. Referring back to Tables I and II, these resultsfor the arrangement of the present technique of incorporating ACG andFCG are now comparable to the conventional prior art models.

TABLE III Word Word Error (%) String Feature Accuracy (%) Sub. Del. InsAccuracy (%) C3: Wireless 95.81 1.68 0.82 1.69 66.48 Baseline C3-1:LPC-CEP, 95.96 1.84 0.80 1.39 67.84 AFG, FCG C3-2: Median 95.98 1.860.78 1.38 68.69 Smoothing C3-3: Gain 96.24 1.69 0.72 1.35 69.77 Scaling

In order to properly analyze these recognition results, it is possibleto use hypothesis tests for analyzing word accuracy (using matched-pairtesting) and string accuracy (using, for example, McNemar's testing). Acomplete description of McNemar's testing as used in speech recognitioncan be found in the article entitled “Some statistical issues in thecomparison of speech recognition algorithms”, by L. Gillick and S. Coxappearing in Proceedings of the ICASSP, p. 532 et seq., May 1989. Formatched-pair testing, the basic premise is to test whether theperformance of a system is comparable to another or not. In other words,a hypothesis H₀ is constructed as follows:

H ₀: μ_(A)−μ_(B)=0,  (3)

where μA and μB represent the mean values of the recognition rates forsystems A and B, respectively. Alternatively, to test the stringaccuracy, McNemar's test can be used to test the statisticalsignificance between the two systems. In particular, the following“null” hypothesis is tested: If a string error occurs from one of thetwo systems, then it is equally likely to be either one of the two. Totest this, N₀₁ is defined as the number of strings that system Arecognizes correctly and system B recognizes incorrectly. Similarly, theterm N₁₀ will define the number of strings that system A recognizesincorrectly and system B recognizes correctly. Then, the test forMcNamara's hypothesis is defined by:

$\begin{matrix}{W = \frac{{{N_{10} - {k/2}}} - {1/2}}{\sqrt{k/4}}} & (4)\end{matrix}$

where k=N₀₁+N₁₀.

As an example, these test statistics can be computed for a “wirelessbaseline” system (C3) and bitstream-based front-end system (C3-3) of thepresent invention, including both ACG and FCG, using the data from TableIII.

TABLE IV Features Matched-pairs McNamara A B (W) (W) C3-3 C3 1.965 2.445

The results of these computations are shown above in Table IV, wherefrom these results it is clear that the incorporation of ACG and FCG inthe arrangement of the present invention provides significantly improvedrecognition performance over the baseline with a confidence of 95%.Moreover, Table V (shown below) illustrates that the proposed front-endof the present invention yields comparable word and string accuracies toconventional wireline performance.

TABLE V Features Matched-pairs McNamara A B (W) (W) C0 C1 3.619 3.607C3-3 C1 3.914 4.388 C3-3 C0 0.328 0.833

The performance of the bitstream-based front end of a speech recognizercan also be analyzed for a “noisy” environment, such as a car, sinceoftentimes a wireless phone is used in such noisy conditions. Tosimulate a noisy environment, a car noise signal can be added to everytest digit string. That is, the speech recognition system is trainedwith “clean” speech signals, then tested with noisy signals. The amountof additive noise can be measured by the segmental signal-to-noise ratio(SNR). Table VI, below, shows the recognition performance comparisonwhen the input SNR varies from 0 dB to 30 dB in steps of 10 dB.

TABLE VI SNR (db) 0 10 20 30 ∞ C0/ Word 14.30 61.82 85.84 95.73 96.17 C0String 0.0 0.51 23.07 65.49 68.48 C0/ Word 21.18 65.59 85.47 94.29 95.16C1 String 0.0 0.51 19.96 55.75 62.32 C3-3 Word 16.82 67.28 90.64 95.2896.24 C3-3 String 0.0 3.62 41.59 63.79 69.77

As shown, for an SNR above 20 dB, the bitstream-based front-endarrangement of the present invention (C-3/C-3) shows a betterperformance than the conventional wireless front end. However, itsperformance is slightly lower than the conventional wireline front end.With lower values of SNR, the arrangement of the present invention doesnot compare as favorably, particularly due to the fact that theinventive front-end utilizes voicing information, but the speech coderitself fails to correctly capture the voicing information at low levelsof SNR.

The utilization of a speech enhancement algorithm with the noisy speechsignal prior to speech coding, however, has been found to improve theaccuracy of the extracted voicing information. An exemplary speechenhancement algorithm that has been found useful with the processing ofnoisy speech is based on minimum mean-square error log-spectralamplitude estimation and has, in fact, been applied to some standardspeech coders. FIG. 8 illustrates speech waveforms implementing suchenhancement under a variety of conditions. In particular, a “clean”speech waveform is shown in FIG. 8( a).

FIG. 8( b) shows the waveform decoded by a conventional IS-641 speechcoder. The “noisy” speech (e.g., contaminated by additive car noise),whose SNR is 20 dB, is shown in FIG. 8( c), and its decoded speechsignal is displayed in FIG. 8( d). This particular type of speechenhancement is applied to the “noisy” signal waveform of FIG. 8( e),where the speech coding is then performed after the enhancement, theresult being shown in FIG. 8( f), which shows that the noise signal isremoved by applying the speech enhancement algorithm.

As mentioned above, channel impairments can be modeled by bit errorinsertion and frame erasure insertion devices, where the number of biterrors and frame erasures depends mainly on the noise, co-channel andadjacent channel interference, and frequency selective fading.Fortunately, most speech coders are combined with a channel coder. Themost sensitive bits are thus strongly protected by the presence of thechannel coder. A “frame erasure” is declared if any of the mostsensitive bits with respect to the channel is in error. In the contextof the bitstream-based arrangement of the present invention, the bitsfor LSP (i.e., bits 1-26) and gain (i.e., bits 121-148) are defined asmost sensitive to channel errors. Therefore, for the purposes of thepresent invention, it is sufficient to consider a “frame erasure”condition to exist if these bits are in error, since the recognitionfeatures in the bitstream-based front end are extracted from these bits.

In the prior art, the speech coding parameters of an erased frame areextrapolated in order to generate the speech signal for the erasedframe. The parameters of erased frames are reconstructed by repeatingthe parameters of the previous frame with scaled-down gain values. Inparticular, the gain values depend on the burstiness of the frameerasure, which is modeled as a finite state machine. That is, if then-th frame is detected as an erased frame, the IS-641 speech coderestimates the spectral parameters by using the following equation:

ω_(n,i) =cω _(n−1,i)+(1−c)ω_(dc,i) , i=1, . . . , p  (5)

where ω_(n,i) is the i-th LSP of the n-th frame and ω_(dc,i) is theempirical mean value of the i-th LSP over a training database and c is aforgetting factor set to a value of 0.9. The ACG and FCG values areobtained by multiplying the predefined attenuation factors to the gainsof the previous frame, and the pitch value is set to the same pitchvalue of the previous frame. The speech signal, using this“extrapolation method” is then reconstructed from these extrapolatedparameters.

As an alternative, the present invention proposes a “deletion method”for overcoming frame erasures in a bitstream-based speech recognitionfront end. Based on the missing feature theory, a decoding algorithm isreformulated for the hidden Markov model (HMM) when a frame erasure isdetected. That is, for a given HMM λ=(A, B, π), the probability of theobservation sequence O={O₁, . . . , O_(N)} is given by:

$\begin{matrix}{{P\left( O \middle| \lambda \right)} = {\sum\limits_{q_{1},\ldots,q_{N}}{\pi_{q\; 1}{b_{q\; 1}\left( o_{1} \right)}a_{q\; 1q\; 2}{b_{q\; 2}\left( o_{2} \right)}\mspace{14mu} \ldots \mspace{14mu} a_{{qN} - {1{qN}}}{b_{qN}\left( o_{N} \right)}}}} & (6)\end{matrix}$

where N is the number of observation vectors in O, (q₁, . . . , q_(N))is defined as a state sequence, and π_(q) is the initial statedistribution. Also, the observation probability of O_(n) at state i isrepresented as follows:

$\begin{matrix}{{{b_{i}\left( o_{n} \right)} = {\sum\limits_{k = 1}^{M}{c_{ik}{N\left( {o_{n};\mu_{i,k};\sum\limits_{ik}} \right)}}}}{where}{{{N\left( {{x;\mu},\sum} \right)} = {\frac{1}{\left( {2\pi} \right)^{\mu/2}{\sum }^{1/2}}\exp \left\{ {{{- 1}/2}\left( {x - \mu} \right)^{2}{\sum\limits^{- 1}\left( {x - \mu} \right)}} \right\}}},}} & (7)\end{matrix}$

M is the number of Gaussian mixtures, and C_(ik) is the k-th mixtureweight of the i-th state. The variables μ and σ define the mean vectorand covariance matrix, respectively.

To understand the “deletion” method of frame erasure method of thepresent invention, presume that the l-th frame is detected as a missingframe The first step in the deletion method is to compute theprobability of only the correct observation vector sequence for themodel λ. The observation vector sequence can be divided into two groupsas follows:

O=(O ⁰ , O ^(m)),  (8)

where o₁εO^(m). From the missing feature theory, the probability to becomputed can be expressed as follows:

P(O|λ)=∫P(O ^(c) ,O ^(m)|λ)dO ^(m).  (9)

Also, for the missing observation vector o₁, it is known that:

∫b _(i)(o ₁)do ₁=1.  (10)

By substituting (6) and (10) into (9), the following relationship isobtained:

$\begin{matrix}{{P\left( O^{c} \middle| \lambda \right)} = {\sum\limits_{q_{1},\ldots,q_{N}}{\pi_{q\; 1}{b_{q\; 1}\left( o_{1} \right)}a_{q\; 1q\; 2}{b_{q\; 2}\left( o_{2} \right)}\mspace{14mu} \ldots \mspace{14mu} a_{{q\; 1} - {{Nq}\; 1}}a_{{q\; 1q\; 1} + n}{b_{{q\; 1} + 1}\left( o_{l + \Gamma} \right)}\mspace{14mu} \ldots \mspace{14mu} a_{{qN} - {1{qN}}}{b_{qN}\left( o_{N} \right)}}}} & (11)\end{matrix}$

It is known that the transition probabilities have less effect in theViterbi search than the observation probabilities. Therefore, it ispossible to set a_(q1−Nq1)=1 . The above equation is then simplyrealized by deleting the vector o₁ in the observation sequence and usingthe conventional HMM decoding procedure.

The deletion method of the present invention can be interpreted in termsof a VFR analysis. In particular, the Euclidean distance of the LSP'sbetween the (n−1)-th and the n-th frames is given by:

$\begin{matrix}{{\sum\limits_{i = 1}^{\rho}\left( {\omega_{n,i} - \omega_{{n - 1},i}} \right)^{2}} = {\left( {I - c} \right){\sum\limits_{i = 1}^{\rho}\left( {\omega_{{n - 1},i} - \omega_{{dc},i}} \right)^{2}}}} & (12)\end{matrix}$

If the distance expressed above is less than or equal to a predefinedthreshold T, the two frames are assumed to be in the steady-state regionand the LSP's of the n-th frame are deleted in the observation sequence.Therefore, if for the threshold T the following is presumed:

$\begin{matrix}{T = {\left( {1 - c} \right)^{2}{\max_{{\lbrack{x_{1},\ldots,x_{p}}\rbrack} \in \Omega}{\sum\limits_{i = 1}^{\rho}\left( {x_{i} - \omega_{{dc},i}} \right)^{2}}}}} & (13)\end{matrix}$

where Ω is a p-dimensional LSP vector space, all of the missing frameswill be deleted.

In terms of computational complexity, it can be concluded that using thedeletion process of the present invention reduces the length of theobservation sequence by N(1−p_(e)), where p_(e) is the frame erasurerate (FER).

To simulate frame erasure conditions, error patterns depending on theFER and its burstiness can be generated for various test strings. Forexample, FIG. 9( a) illustrates the word error rate (WER) when therandom FER varies from 3% to 20%. An PER of 0% is defined as a “clean”environment, where a 3% FER is considered typical of a conventional TDMAchannel. At 3% FER, the WER's are increased by 6.4% and 5.3% for thebitstream-based front-end of the present invention, utilizing theconventional “extrapolation” frame erasure method and the inventive“deletion” method, respectively, where the deletion method has beenfound to have a higher deletion error and lower insertion andsubstitution error than the extrapolation method.

FIG. 9( b) illustrates the WER as a function of the burstiness of theFER when the FER is 3% (the “burstiness” being defined as b for the sakeof simplicity). Similar to the random FER case, the WER's of thebitstream-based front-ends are smaller than those associated withdecoded speech-based front-ends. Comparing the WER performance at b=0.99to that under a “clean” environment, the decoded speech-based front-endincreases the WER by 24.3%, while the bitstream-based front-ends withthe extrapolation method and with the deletion method increase the WERby 19.7% and 22.1%, respectively. The inventive deletion method gives aslightly worse performance than the extrapolation method when b is largesince the deletion method increases the deletion errors as b increases.

FIG. 10 illustrates the ratios of processing time between theextrapolation method and the deletion method for each FER and level ofburstiness. For the purposes of this graph, the processing time wascalculated by performing recognition experiments overall all the testdata on the same machine. As shown, the results verify that the proposeddeletion method has less computational complexity than the extrapolationmethod.

While the exemplary embodiments of the present invention have beendescribed above in detail, it is to be understood that such descriptiondoes not limit the scope of the present invention, which may bepracticed in a variety of embodiments. Indeed, it will be understood bythose skilled in the art that changes in the form and details of theabove description may be made therein without departing from the scopeand spirit of the invention.

1. A method of generating speech coding parameters of an erased frame ina bitstream-based front end of a speech recognition system, the methodcomprising the steps of: detecting an erased frame; computing theprobability of a correct observation sequence from the group O=(O⁰,O^(m)), where the probability is defined as:${P\left( O^{c} \middle| \lambda \right)} = {\sum\limits_{q_{1},\ldots,q_{N}}{\pi_{q\; 1}{b_{q\; 1}\left( o_{1} \right)}a_{q\; 1q\; 2}{b_{q\; 2}\left( o_{2} \right)}\mspace{14mu} \ldots \mspace{14mu} a_{{q\; 1} - {{Nq}\; 1}}a_{{q\; 1q\; 1} + n}{b_{{q\; 1} + 1}\left( o_{l + \Gamma} \right)}\mspace{14mu} \ldots \mspace{14mu} a_{{qN} - {1{qN}}}{b_{qN}\left( o_{N} \right)}}}$deleting the erased frame observation vector; and decoding with astandard hidden Markov model process.
 2. The method as defined in claim1 wherein in performing the detection, the following steps areperformed: measuring the Euclidean distance between the line spectrumpairs (LSPs) of contiguous frames; defining a steady-state threshold T;and deleting one frame of the contiguous frames when the Euclideandistance is less than the threshold.
 3. The method as defined in claim 2wherein the following relation is used to define the Euclidean distance:${\sum\limits_{i = 1}^{\rho}\left( {\omega_{n,i} - \omega_{{n - 1},i}} \right)^{2}} = {\left( {I - c} \right){\sum\limits_{i = 1}^{\rho}\left( {\omega_{{n - 1},i} - \omega_{{dc},i}} \right)^{2}}}$4. The method as defined in claim 1 wherein in detecting a frameerasure, an erasure is declared when the bits most sensitive to errorwithin a frame are determined to be in error.
 5. The method as definedin claim 4 wherein the bits most sensitive to error in a frame in abitstream-based speech recognition system include the line spectrum pairinformation bits and the gain information bits.